Complex data center environments contain a large number of infrastructure elements which interact to deliver services such as email, e-commerce, web, and a wide variety of enterprise applications. Failure of any component in the data center may or may not have an impact on service availability, capacity, or performance. Static mapping of infrastructure and application components to services is a well understood process, however the introduction of dynamic virtualized systems and cloud computing environments has created an environment where these mappings can change rapidly at any time.
Traditional systems such as EMC SMARTS or IBM NetCool have been designed to address Impact Analysis for services deployed in traditional fixed infrastructure data centers. In this environment dependencies are well known when policies are defined, and as such it is possible to define event patterns or “fingerprints” which have some impact on service availability, capacity, or performance.
The nature of dynamic data center environments facilitates rapid deployment of virtualized infrastructure or automated migration of virtual machines in response to fluctuating demand for application services. As a result traditional Impact Analysis and Service Assurance engines based on infrastructure “fingerprinting” break due to the fact that policies are not dynamically updated as service dependencies change.
Therefore, to address the above described problems and other problems, what is needed is a method and apparatus that obviates the need to have detailed knowledge of an entire dependency tree when determining service state at any given time.